Machine Learning Classifiers

ML Classifier models that are evaluated to predict the accurate output

Linear SVC

A Linear SVC's (Support Vector Classifier) goal is to split or categorize the data you supply by producing a "best fit" hyperplane. You may then provide some variables to your classification model to get the "predicted" class after acquiring the hyperplane.

Logistic Regression

A type of linear classification model is logistic regression. In this model, a logistic function is used to simulate the probabilities describing the potential outcomes of a single experiment. The logistic function is a sigmoid function, making it perfect for classification because it accepts any real input and produces a value between 0 and 1.

SVM

A collection of mathematical operations known as the kernel are used by SVM algorithms. Data is inputted into the kernel, which then transforms it into the desired form. Different kernel functions are used by various SVM algorithms. There are various forms of these functions.

Decision Tree

A hierarchical model called decision trees is also referred to as classification and regression trees. They have the ability to forecast response based on data. The decision trees' properties are translated into nodes. The potential output values are represented by the tree's edges. From the root to the leaf node, each branch of a tree represents a classification rule.

Random Forest

Unpruned supply or descends like a bootstrapping method with different decision trees is what makes up Random Forest. Each tree is based on the estimates of the randomly and independently chosen vector. When compared to a single tree classifier, Random Forest consistently provides a huge improvement. With the help of the algorithm, each tree is created.

Results

We used the Linear SVC model because it has the highest accuracy among the models that we have tested.

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